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Artículos de revistas sobre el tema "Face recognition"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 9 de junio de 2025

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1

Deshmukh, Sagar, Sanjay Rawat, and Shubhangi Patil. "Face Recognition Technology." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1612–13. http://dx.doi.org/10.31142/ijtsrd14331.

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Yadav, Rakeshkumar H., Brajgopal Agarwal, and Sheeba James. "Face Recognition System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1815–18. http://dx.doi.org/10.31142/ijtsrd14453.

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Ounachad, Khalid, Mohamed Oualla, Abdelalim Sadiq, and Abdelghani Sohar. "Face Sketch Recognition: Gender Classification and Recognition." International Journal of Psychosocial Rehabilitation 24, no. 03 (February 18, 2020): 1073–85. http://dx.doi.org/10.37200/ijpr/v24i3/pr200860.

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V, Prathama, and Thippeswamy G. "Age Invariant Face Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 971–76. http://dx.doi.org/10.31142/ijtsrd23572.

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Lakshmi, Dr D. Vijaya, Srija Reddy Ardha, and Anand karthik Azmeera. "Cross Age Face Recognition." International Journal of Research Publication and Reviews 6, no. 4 (April 2025): 4381–407. https://doi.org/10.55248/gengpi.6.0425.1466.

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Rakeshkumar, H. Yadav, Agarwal Brajgopal, and James Sheeba. "Face Recognition System." International Journal of Trend in Scientific Research and Development 2, no. 4 (June 21, 2018): 1815–18. https://doi.org/10.31142/ijtsrd14453.

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Face is a important part through which we can identify who we are and how people identify us. Face is a persons most valuable and unique physical characteristics through which we can identify someone very easily. While humans have the innate ability to distinguish the different faces for millions of years for adding the new technology computers are just now catching up. A face recognition system is a computer application that is capable of identifying or verifying the person from a digital image or a video frame from video source. One of the way is to do this is by compare with the selected facial features and also a face database. Humans are having good tendency to recognizing faces. Rakeshkumar H Yadav | Brajgopal Agarwal | Sheeba James "Face Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd14453.pdf
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7

Telugu, Maddileti, Shriphad Rao G., Sai Madhav Vaddemani, and Sharan Ganti. "Home Security using Face Recognition Technology." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (December 30, 2019): 678–82. https://doi.org/10.35940/ijeat.B3917.129219.

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Face is the easiest way to penetrate each other's personal identity. Face recognition is a method of personal identification using the personal characteristics of an individual to decide the identification of a person. The method of human face recognition consists basically of two levels, namely face detection and face recognition. There are three types of methods that are currently popular in the developed face recognition pattern, those are Eigen faces algorithm, Fisher faces algorithm and CNN neural network for face recognition
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8

Patel, Ibrahim, Raghavendra Kulkarni, and Dr P. Nageswar Rao. "Robust Singular Value Decomposition Algorithm for Unique Faces." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (June 21, 2018): 596–603. http://dx.doi.org/10.24297/ijct.v4i2c1.4178.

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It has been read and also seen by physical encounters that there found to be seven near resembling humans by appearance .Many a times one becomes confused with respect to identification of such near resembling faces when one encounters them. The recognition of familiar faces plays a fundamental role in our social interactions. Humans are able to identify reliably a large number of faces and psychologists are interested in understanding the perceptual and cognitive mechanisms at the base of the face recognition process. As it is needed that an automated face recognition system should be faces specific, it should effectively use features that discriminate a face from others by preferably amplifying distinctive characteristics of face. Face recognition has drawn wide attention from researchers in areas of machine learning, computer vision, pattern recognition, neural networks, access control, information security, law enforcement and surveillance, smart cards etc. The paper shows that the most resembling faces can be recognized by having a unique value per face under different variations. Certain image transformations, such as intensity negation, strange viewpoint changes, and changes in lighting direction can severely disrupt human face recognition. It has been said again and again by research scholars that SVD algorithm is not good enough to classify faces under large variations but this paper proves that the SVD algorithm is most robust algorithm and can be proved effective in identifying faces under large variations as applicable to unique faces. This paper works on these aspects and tries to recognize the unique faces by applying optimized SVD algorithm.
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9

Reddy, Mr B. Ravinder, V. Akhil, and G. Sai Preetham P. Sai Poojitha. "Profile Identification through Face Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1482–83. http://dx.doi.org/10.31142/ijtsrd23439.

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10

A, ASLIM S., and Dr A. MYTHILI. "Automation Attendance Using Face Recognition." International Journal of Research Publication and Reviews 6, no. 4 (April 2025): 257–60. https://doi.org/10.55248/gengpi.6.0425.1314.

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11

Aishwarya, T. G., Veena N, and Pallerla Asritha. "Face Recognition Attendance Management System." International Journal of Research Publication and Reviews 6, no. 5 (May 2025): 11049–52. https://doi.org/10.55248/gengpi.6.0525.1888.

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12

Garg, Deepika. "Face Recognition." IOSR Journal of Engineering 02, no. 07 (July 2012): 128–33. http://dx.doi.org/10.9790/3021-0271128133.

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13

Zhao, W., R. Chellappa, P. J. Phillips, and A. Rosenfeld. "Face recognition." ACM Computing Surveys 35, no. 4 (December 2003): 399–458. http://dx.doi.org/10.1145/954339.954342.

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14

Gross, Charles G., and Justine Sergent. "Face recognition." Current Biology 2, no. 5 (May 1992): 235. http://dx.doi.org/10.1016/0960-9822(92)90354-d.

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.Gross, Charles G., and Justine Sergent. "Face recognition." Current Opinion in Neurobiology 2, no. 2 (April 1992): 156–61. http://dx.doi.org/10.1016/0959-4388(92)90004-5.

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16

Prof Sami M. Halwani, Prof M. V. Ramana Murthy, and Prof S. B. Thorat. "Laplacian Faces: A Face Recognition Tool." International Journal of Networked Computing and Advanced Information Management 2, no. 1 (April 30, 2012): 1–7. http://dx.doi.org/10.4156/ijncm.vol2.issue1.1.

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17

Schwartz, Linoy, and Galit Yovel. "Are Faces Important for Face Recognition?" Journal of Vision 15, no. 12 (September 1, 2015): 703. http://dx.doi.org/10.1167/15.12.703.

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18

Tovée, Martin J. "Face Recognition: What are faces for?" Current Biology 5, no. 5 (May 1995): 480–82. http://dx.doi.org/10.1016/s0960-9822(95)00096-0.

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19

He, Yunhui, Li Zhao, and Cairong Zou. "Face recognition using common faces method." Pattern Recognition 39, no. 11 (November 2006): 2218–22. http://dx.doi.org/10.1016/j.patcog.2006.04.037.

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20

米, 勇. "Face Recognition Based on Feature Faces." Computer Science and Application 09, no. 01 (2019): 127–31. http://dx.doi.org/10.12677/csa.2019.91015.

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21

C.G., Ezekwe I. C. Ituma P. I. Okwu. "FACE PROCESSING AND RECOGNITION BASED CLASSROOM ATTENDANCE SYSTEM." Global Journal of Engineering Science and Research Management 5, no. 4 (April 20, 2018): 12–23. https://doi.org/10.5281/zenodo.1222126.

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Educational institutions’ administrators in our country and the whole world are concerned about regularity of student attendance. Student overall academic performance is affected by it. The conventional method of taking attendance by calling names or signing on paper is very time consuming, and hence inefficient. This problem gave birth to research on Radio frequency identification (RFID) authentication with face processing and recognition though in this paper we basically highlighted on the face processing and recognition. The system is made up of a camera which take the photos of individuals and a computer unit which performs face detection (locating faces from the image removing the background information) and face recognition (identifying the persons)  First, face images are acquired using webcam to create the database. Face recognition system will detect the location of face in the image and will extract the features from the detected faces. As a result of feature extraction process, templates or eigenfaces are generated which are reduced set of data that represents the unique features of enrolled user’s face. These templates are stored in database after eigenface calculation. The basis of the eigenfaces calculation in this work is the Principal Component Analysis (PCA). The Principal Component Analysis is a method of projection to a subspace and is widely used in pattern recognition. The objectives of PCA are the replacement of correlated vectors of large dimensions with the uncorrelated vectors of smaller dimensions and to calculate a basis for the data set. C# was used for serial communication, the image training, detection and recognition and for the application interfaces, and in connection other physical components. At the end of this research work, we were able to achieve a classroom attendance system that uses the students’ images for authentication and at the same time, it is able to have high level of security and privacy because another student can never take attendance for the other.
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22

Xiong, Yijie. "Face recognition based on machine learning." Applied and Computational Engineering 6, no. 1 (June 14, 2023): 1100–1105. http://dx.doi.org/10.54254/2755-2721/6/20230407.

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Due to its widespread use, face recognition has emerged in the past 20 years as one of the most pervasive biometric identification technology disciplines. This paper briefly summarizes the history of face recognitions development, identifies the technologys present use cases, introduces the main methods of face recognition in detail from the perspective of machine learning and prospects for the future development of this technology. The result shows that this technology still faces many challenges, such as the problem of recognizing different expressions on the same face, the problem of recognizing twins and similar faces, the problem of using the color information of color face images efficiently and so on.
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23

Sagar, Deshmukh, Rawat Sanjay, and Patil Shubhangi. "Face Recognition Technology." International Journal of Trend in Scientific Research and Development 2, no. 4 (December 19, 2019): 1612–13. https://doi.org/10.31142/ijtsrd14331.

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The biometric is a study of human behavior and features. Face recognition is a technique of biometric. Various approaches are used for it. Face recognition is emerging branch of biometric for security as no faces can be defeated as a security approach. So, how we can recognize a face with the help of computers is given in this paper. The typical way that a FRS can be used for identification purposes. The effectiveness of the whole system is highly dependent on the quality and characteristics of the captured face image. The process begins with face detection and extraction from the larger image, which generally contains a background and often more complex patterns and even other faces. A survey for all these techniques is in this paper for analyzing various algorithms and methods. Sagar Deshmukh | Sanjay Rawat | Shubhangi Patil "Face Recognition Technology" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd14331.pdf
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24

Afrin, Sadia, Maria Tasnim, and Md Rafiqul Islam. "Human Face Recognition Using Eigen Vector-Based Recognition System." International Journal of Research and Scientific Innovation X, no. VI (2023): 127–34. http://dx.doi.org/10.51244/ijrsi.2023.10617.

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Face recognition is an algorithm that can recognize or verify a query face among a large number of faces in the enrollment database. Face recognition is a crucial and difficult area of computer vision. This study demonstrates a system that can recognize a human face by comparing the facial structure to that of another individual or a well-known individual, which is accomplished by the use of frontal several summarizations. Many researchers have done their work on face recognition and also applied it by using different methods. We made use of an eigenvector-based recognition system as a method for recognizing faces. The face recognition system is highly accurate and is one of the most powerful surveillance tools ever made. But this face recognition technology is quite costly for developing countries like Bangladesh. In this study, we have used a face recognition system for our security purpose using an eigenvector-based face recognition system with the help of MATLAB software and a Raspberry Pi camera for security purposes which minimizes the cost, and this process we have used is quite affordable
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25

Anjali, Muneshwar, and Vattam Prof.Jayarajesh. "Face Detection System with Face Recognition." International Organization of Research & Development (IORD) 9, no. 1 (June 23, 2021): 5. https://doi.org/10.5281/zenodo.5016190.

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The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses the personal characteristics of a person to identify the person's identity. The human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognizes a face as individuals. The stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition patterns, namely, the Eigenface method and the Fisherface method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigenfaces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project's face detection system with face recognition is Image processing. The software required for this project is Matlab software.
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26

Abbas, Hawraa H., Bilal Z. Ahmed, and Ahmed Kamil Abbas. "3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 28–37. http://dx.doi.org/10.2478/cait-2019-0013.

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Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.
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27

Kaur, Puneet. "Exploring the Challenges of Aadhaar based Face Recognition in Unrestricted Environments." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (January 22, 2025): 1–9. https://doi.org/10.55041/ijsrem41021.

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- Technology improvements have resulted in a high criminal rate, which has raised serious concerns about security-related issues. Face recognition can be used to identify people because every person tends to possess a particular attribute. One of the main applications of facial recognition is in the field of video surveillance. To reduce the increasing criminal rates, this technology is responsible for extracting features from the human face and further identifying them. The CCTV footage might also be used to identify suspects at a crime scene. However, the criminals would be found by recognizing and comparing the suspect's face to the database repository that would include data on people based on their Aadhaar card. Aadhaar photos can be used to identify and recognize faces in a facial recognition system since Aadhaar is a unique identification system in India and mandatory for every Indian resident. However, there are many challenges faced by Aadhaar based face recognition. Face recognition in uncontrolled environments like public surveillance faces significant challenges such as low-resolution images, pose variations, and masked faces. This paper highlights the challenges faced by Aadhaar enabled face recognition in surveillance scenes. This article aims to provide a brief survey of the Aadhaar-enabled face recognition system for criminal identification and recognizing individuals. There is limited work with the Aadhaar dataset due to privacy and security reasons. This article also emphasizes the difficulties that will be encountered in implementing facial recognition as well as strategies for enhancing it while taking various tradeoffs into account. It also recommends opportunities for further study in the application of facial identification in other fields. Key Words: Aadhaar, Surveillance, Face recognition, Criminals
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28

Priya, B. Lakshmi, and Dr M. Pushpa Rani Rani. "Face Recognition System Techniques and Approaches." Indian Journal of Applied Research 4, no. 4 (October 1, 2011): 109–13. http://dx.doi.org/10.15373/2249555x/apr2014/32.

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29

R.S., Dr Sabeenian. "Attendance Authentication System Using Face Recognition." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (March 31, 2020): 1235–48. http://dx.doi.org/10.5373/jardcs/v12sp4/20201599.

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30

Mishra, K. Ravikanth, D. Brahmeswara Rao, and A. Dinesh Chowdary. "Student Library Attendance using Face Recognition." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1238–40. http://dx.doi.org/10.31142/ijtsrd11281.

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31

Malkapurkar, Anagha V., and Prof Sachin Murarka. "Using LBP histogram for Face Recognition." International Journal of Scientific Research 1, no. 7 (June 1, 2012): 176–77. http://dx.doi.org/10.15373/22778179/dec2012/64.

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32

Kanth, Pooja L., and Salva Biswal. "Attendance Marking System Using Face Recognition." Indian Journal of Science and Technology 12, no. 48 (December 20, 2019): 1–3. http://dx.doi.org/10.17485/ijst/2019/v12i48/145821.

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33

Dhaygude, Rohit H., Avinash M. Gaikwad, Suraj Y. Gawali, and Prof Jawed H. Shaikh. "Smart Home Security & Face Recognition." International Journal of Research Publication and Reviews 5, no. 4 (April 11, 2024): 4009–13. http://dx.doi.org/10.55248/gengpi.5.0424.1023.

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34

Rhodes, Gillian. "Adaptive Coding and Face Recognition." Current Directions in Psychological Science 26, no. 3 (June 2017): 218–24. http://dx.doi.org/10.1177/0963721417692786.

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Face adaptation generates striking face aftereffects, but is this adaptation useful? The answer appears to be yes, with several lines of evidence suggesting that it contributes to our face-recognition ability. Adaptation to face identity is reduced in a variety of clinical populations with impaired face recognition. In addition, individual differences in face adaptation are linked to face-recognition ability in typical adults. People who adapt more readily to new faces are better at recognizing faces. This link between adaptation and recognition holds for both identity and expression recognition. Adaptation updates face norms, which represent the typical or average properties of the faces we experience. By using these norms to code how faces differ from average, the visual system can make explicit the distinctive information that we need to recognize faces. Thus, adaptive norm-based coding may help us to discriminate and recognize faces despite their similarity as visual patterns.
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35

Bhange, Prof Anup. "Face Detection System with Face Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 1095–100. http://dx.doi.org/10.22214/ijraset.2022.39976.

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Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. Human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is recognition, which recognize (by comparing face with picture or either with image captured through webcam) a face as an individual. In face detection and recognition technology, it is mainly introduced from the OpenCV method. Face recognition is one of the much-studied biometrics technology and developed by experts. The area of this project face detection system with face recognition is Image processing. The software requirement for this project is Python. Keywords: face detection, face recognition, cascade_classifier, LBPH.
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36

Romanyuk, Olexandr N., Sergey I. Vyatkin, Sergii V. Pavlov, Pavlo I. Mykhaylov, Roman Y. Chekhmestruk, and Ivan V. Perun. "FACE RECOGNITION TECHNIQUES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 1 (March 30, 2020): 52–57. http://dx.doi.org/10.35784/iapgos.922.

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The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.
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37

Said, Ebrahem, and Mona Nasr. "Face Recognition System." International Journal of Advanced Networking and Applications 12, no. 02 (2020): 4567–74. http://dx.doi.org/10.35444/ijana.2020.12205.

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38

Rajput, Ankit. "Face Recognition Technology." International Journal for Research in Applied Science and Engineering Technology 7, no. 3 (March 31, 2019): 859–62. http://dx.doi.org/10.22214/ijraset.2019.3150.

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39

Sabharwal, Himani, and Akash Tayal. "Human Face Recognition." International Journal of Computer Applications 104, no. 11 (October 18, 2014): 1–3. http://dx.doi.org/10.5120/18243-9173.

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40

Liu, Tongyang, Xiaoyu Xiang, Qian Lin, and Jan P. Allebach. "Face Set Recognition." Electronic Imaging 2019, no. 8 (January 13, 2019): 400–1. http://dx.doi.org/10.2352/issn.2470-1173.2019.8.imawm-400.

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41

Saxenna, Yasharth. "Face Recognition System." International Journal for Research in Applied Science and Engineering Technology 8, no. 7 (July 31, 2020): 1883–85. http://dx.doi.org/10.22214/ijraset.2020.30704.

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42

Tang, X., and X. Wang. "Face Sketch Recognition." IEEE Transactions on Circuits and Systems for Video Technology 14, no. 1 (January 2004): 50–57. http://dx.doi.org/10.1109/tcsvt.2003.818353.

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43

S.G, Rajeshwari. "Human Face Recognition." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 638–43. http://dx.doi.org/10.22214/ijraset.2020.6104.

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44

Dzhangarov, A. I., M. A. Suleymanova, and A. L. Zolkin. "Face recognition methods." IOP Conference Series: Materials Science and Engineering 862 (May 28, 2020): 042046. http://dx.doi.org/10.1088/1757-899x/862/4/042046.

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45

Liu, Yun-Fu, Jing-Ming Guo, Po-Hsien Liu, Jiann-Der Lee, and Chen-Chieh Yao. "Panoramic Face Recognition." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 8 (August 2018): 1864–74. http://dx.doi.org/10.1109/tcsvt.2017.2693682.

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46

Moghaddam, Baback, Tony Jebara, and Alex Pentland. "Bayesian face recognition." Pattern Recognition 33, no. 11 (November 2000): 1771–82. http://dx.doi.org/10.1016/s0031-3203(99)00179-x.

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47

Russell, R., B. Duchaine, and K. Nakayama. "Extraordinary face recognition." Journal of Vision 7, no. 9 (March 23, 2010): 629. http://dx.doi.org/10.1167/7.9.629.

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48

Voth, D. "Face recognition technology." IEEE Intelligent Systems 18, no. 3 (May 2003): 4–7. http://dx.doi.org/10.1109/mis.2003.1200719.

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Bruce, Vicki, and Andy Young. "Understanding face recognition." British Journal of Psychology 77, no. 3 (August 1986): 305–27. http://dx.doi.org/10.1111/j.2044-8295.1986.tb02199.x.

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Kroeker, Kirk L. "Face recognition breakthrough." Communications of the ACM 52, no. 8 (August 2009): 18–19. http://dx.doi.org/10.1145/1536616.1536623.

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